ASSESSING VISUO-SPATIAL NEGLECT THROUGH FEATURE SELECTION FROM SHAPE DRAWING PERFORMANCE AND SEQUENCE ANALYSIS

2004 ◽  
Vol 18 (07) ◽  
pp. 1253-1266 ◽  
Author(s):  
SAMUEL CHINDARO ◽  
RICHARD GUEST ◽  
MICHAEL FAIRHURST ◽  
JONATHAN POTTER
Keyword(s):  
Markov Models ◽  
Diagnostic Methods ◽  
Spatial Neglect ◽  
Dynamic Features ◽  
Combination Strategy ◽  
Standard Assessment ◽  
Recognition Ability ◽  
Pencil And Paper ◽  
And Performance ◽  
Drawing Tasks

The reported work aims to objectively and accurately assess the post-stroke clinical condition of visuo-spatial neglect using a series of standardized geometric shape drawing tasks. We present a method implementing existing pencil-and-paper diagnostic methods and define a set of static and dynamic features that can be extracted from drawing responses captured online using a graphics tablet. We also present a method for automatically assessing the constructional sequence of the drawing using Hidden Markov Models. The method enables the automated extraction, position identification and drawing order of individual sides of a shape within a drawing. Discrimination between two populations (a neglect population and stroke subjects without neglect as determined by existing standard assessment methods) using a combination of performance features and constructional sequence is examined across three separate drawing tasks. Results from experimentation show how a combination of sequence and performance features is able to generalize across a wide variety of input samples and obtain a diagnostic classification which can be used alongside other forms of conventional assessment. Furthermore, the application of a multi-classifier combination strategy leads to a significant increase in recognition ability.

scholarly journals The burden of congenital Chagas disease and implementation of molecular diagnostic tools in Latin America

2018 ◽  
Vol 3 (5) ◽  
pp. e001069 ◽  
Author(s):  
Albert Picado ◽  
Israel Cruz ◽  
Maël Redard-Jacot ◽  
Alejandro G Schijman ◽  
Faustino Torrico ◽  
...  
Keyword(s):  
Latin America ◽  
Chagas Disease ◽  
Diagnostic Algorithm ◽  
Diagnostic Methods ◽  
Diagnosis And Treatment ◽  
Molecular Diagnostic ◽  
Diagnostic Tools ◽  
Molecular Tests ◽  
Epidemiological Impact ◽  
And Performance

It is estimated that between 8000 and 15 000 Trypanosoma cruzi infected babies are born every year to infected mothers in Chagas disease endemic countries. Currently, poor access to and performance of the current diagnostic algorithm, based on microscopy at birth and serology at 8–12 months after delivery, is one of the barriers to congenital Chagas disease (CCD) control. Detection of parasite DNA using molecular diagnostic tools could be an alternative or complement to current diagnostic methods, but its implementation in endemic regions remains limited. Prompt diagnosis and treatment of CCD cases would have a positive clinical and epidemiological impact. In this paper, we analysed the burden of CCD in Latin America, and the potential use of molecular tests to improve access to early diagnosis and treatment of T. cruzi infected newborns.

The Arrow-Dot Test as a Predictor of Police Officer Performance

1977 ◽  
Vol 45 (3) ◽  
pp. 683-693 ◽  
Author(s):  
Melany E. Baehr ◽  
Ernest C. Froemel
Keyword(s):  
Police Officers ◽  
Test Battery ◽  
Performance Criteria ◽  
Regression Analyses ◽  
Potential Predictor ◽  
Officer Performance ◽  
Pencil And Paper ◽  
And Performance ◽  
Project Personnel ◽  
Selection Of

In the course of validating a test battery for the selection of police officers (Baehr, Furcon, & Froemel, 1969), project personnel were searching for objectively scored, pencil-and-paper measures of behavior variables. One product of this search was an adaptation of the Arrow-Dot subtest of the four-part IES test battery (Dombrose & Slobin, 1958). This adaptation was designed for group administration and for improved reliability as a possible predictor of performance. Intercorrelational analysis and factor analysis of intercorrelations with other potential predictor variables and performance criteria provided some information on the nature of the Arrow-Dot scores. The test's ability to predict police officers' performance was assessed through multiple regression analyses of its scores both as independent predictors and as contributors of unique variance in a wider predictor battery. Its usefulness for diagnosis of specific problems in police officers' performance was also investigated. Application in five additional studies permitted an exploration of the “characteristic” Arrow-Dot dimension profile for police officers.

scholarly journals The 20-item prosopagnosia index (PI20): a self-report instrument for identifying developmental prosopagnosia

2015 ◽  
Vol 2 (6) ◽  
pp. 140343 ◽  
Author(s):  
Punit Shah ◽  
Anne Gaule ◽  
Sophie Sowden ◽  
Geoffrey Bird ◽  
Richard Cook
Keyword(s):  
Face Recognition ◽  
Self Report ◽  
New Instrument ◽  
Recognition Ability ◽  
Unfamiliar Face ◽  
Computer Based ◽  
Objective Tests ◽  
And Performance ◽  
Reliable Classification ◽  
Perceptual Impairment

Self-report plays a key role in the identification of developmental prosopagnosia (DP), providing complementary evidence to computer-based tests of face recognition ability, aiding interpretation of scores. However, the lack of standardized self-report instruments has contributed to heterogeneous reporting standards for self-report evidence in DP research. The lack of standardization prevents comparison across samples and limits investigation of the relationship between objective tests of face processing and self-report measures. To address these issues, this paper introduces the PI20; a 20-item self-report measure for quantifying prosopagnosic traits. The new instrument successfully distinguishes suspected prosopagnosics from typically developed adults. Strong correlations were also observed between PI20 scores and performance on objective tests of familiar and unfamiliar face recognition ability, confirming that people have the necessary insight into their own face recognition ability required by a self-report instrument. Importantly, PI20 scores did not correlate with recognition of non-face objects, indicating that the instrument measures face recognition, and not a general perceptual impairment. These results suggest that the PI20 can play a valuable role in identifying DP. A freely available self-report instrument will permit more effective description of self-report diagnostic evidence, thereby facilitating greater comparison of prosopagnosic samples, and more reliable classification.

scholarly journals Reliability in One-Repetition Maximum Performance in People with Parkinson's Disease

2012 ◽  
Vol 2012 ◽  
pp. 1-6 ◽  
Author(s):  
Thomas A. Buckley ◽  
Christopher J. Hass
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Knee Flexion ◽  
Muscular Strength ◽  
Functional Performance ◽  
High Reliability ◽  
Knee Extension ◽  
Repetition Maximum ◽  
Standard Assessment ◽  
And Performance

Strength training is an effective modality to improve muscular strength and functional performance in people with Parkinson's disease (PWP). One-repetition maximum (1-RM) is the gold standard assessment of strength; however, PWP suffer from day-to-day variations in symptom severity and performance characteristics, potentially adversely affecting the reliability of 1-RM performance. Herein, we assessed the reliability of 1-RM in PWP. Forty-six participants completed two sessions of 1-RM testing of knee extension, knee flexion, chest press, and biceps curl at least 72 hours apart. Significantly differences between testing sessions were identified for knee extension (P< 0.001), knee flexion (P= 0.042), and biceps curl (P= 0.001); however, high reliability (ICC > 0.90) was also identified between sessions. Interestingly, almost third of subjects failed to perform better on the second testing session. These findings suggest that 1-RM testing can be safely performed in PWP and that disease-related daily variability may influence 1-RM performance.

scholarly journals dfgcompare: a library to support process variant analysis through Markov models

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Amin Jalali ◽  
Paul Johannesson ◽  
Erik Perjons ◽  
Ylva Askfors ◽  
Abdolazim Rezaei Kalladj ◽  
...  
Keyword(s):  
Performance Metrics ◽  
Markov Models ◽  
Process Analysis ◽  
Cohort Analysis ◽  
Data Driven ◽  
Software Support ◽  
Cohort Comparison ◽  
Analysis Technique ◽  
Variant Analysis ◽  
And Performance

Abstract Background Data-driven process analysis is an important area that relies on software support. Process variant analysis is a sort of analysis technique in which analysts compare executed process variants, a.k.a. process cohorts. This comparison can help to identify insights for improving processes. There are a few software supports to enable process cohort comparison based on the frequencies of process activities and performance metrics. These metrics are effective in cohort analysis, but they cannot support cohort comparison based on the probability of transitions among states, which is an important enabler for cohort analysis in healthcare. Results This paper defines an approach to compare process cohorts using Markov models. The approach is formalized, and it is implemented as an open-source python library, named dfgcompare. This library can be used by other researchers to compare process cohorts. The implementation is also used to compare caregivers’ behavior when prescribing drugs in the Stockholm Region. The result shows that the approach enables the comparison of process cohorts in practice. Conclusions We conclude that dfgcompare supports identifying differences among process cohorts.

F0 Modeling for Isarn Speech Synthesis using Deep Neural Networks and Syllable-level Feature Representation

2020 ◽  
Vol 17 (6) ◽  
pp. 906-915
Author(s):  
Pongsathon Janyoi ◽  
Pusadee Seresangtakul
Keyword(s):  
Speech Synthesis ◽  
Deep Neural Networks ◽  
Markov Models ◽  
Feature Representation ◽  
Context Dependency ◽  
Dynamic Features ◽  
Synthesis System ◽  
Proposed Model ◽  
Training Sets

The generation of the fundamental frequency (F0) plays an important role in speech synthesis, which directly influences the naturalness of synthetic speech. In conventional parametric speech synthesis, F0 is predicted frame-by-frame. This method is insufficient to represent F0 contours in larger units, especially tone contours of syllables in tonal languages that deviate as a result of long-term context dependency. This work proposes a syllable-level F0 model that represents F0 contours within syllables, using syllable-level F0 parameters that comprise the sampling F0 points and dynamic features. A Deep Neural Network (DNN) was used to represent the relationships between syllable-level contextual features and syllable-level F0 parameters. The proposed model was examined using an Isarn speech synthesis system with both large and small training sets. For all training sets, the results of objective and subjective tests indicate that the proposed approach outperforms the baseline systems based on hidden Markov models and DNNS that predict F0 values at the frame level

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